Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Uncertainty in Remote Sensing Image Analysis II
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Uncertainty in Remote Sensing Image Analysis II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 1956

Special Issue Editors

Dr. Penglin Zhang

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: semantic segmentation of remote sensing images; uncertainty analysis of remote sensing images; change detection using of remote sensing images; reliability analysis of spatio-temporal data
Dr. Qi Zhang

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: image processing and uncertainty analysis; quality evaluation and analysis in remote sensing; uncertainty modeling and quantification; uncertainty control; remote sensing intelligent interpretation; deep learning; image classification and change detection
Prof. Dr. Jon Atli Benediktsson

grade E-Mail Website
Guest Editor
Faculty of Electrical and Computer Engineering, University of Iceland, IS 102 Reykjavík, Iceland
Interests: remote sensing; image analysis; pattern recognition; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing has been widely used in various fields, such as agriculture, ecology, and urban planning, to extract useful information from satellite or aerial imagery. It should be noted that the reliability of remote sensing data is essential for further applications and scientific decision making. However, the complexity of natural environments and remote sensing imaging processes determines that uncertainty is an inherent attribute of remote sensing data. Moreover, different degrees of uncertainty may be introduced at various stages of processing and analysis. These uncertainties decrease the accuracy and reliability of the results of analyses and applications to different extents, thus hindering the improvement of remote sensing technology. Therefore, uncertainty has always been a key issue and research focus in the field of remote sensing.

In recent years, significant progress on quantifying, controlling, modeling, and analyzing the uncertainties in remote sensing image analysis and applications has been made. We invite researchers and practitioners to submit research papers to this Special Issue on “Uncertainty in Remote Sensing Image Analysis II”. We welcome contributions that are not simply limited to the following topics:

  • Sources of uncertainty in remote sensing data;
  • Methodologies for uncertainty quantification in remote sensing image analysis and applications;
  • Uncertainty evaluation in image interpretation and analysis;
  • Algorithms or tools developed for uncertainty modeling;
  • Uncertainty reduction in remote sensing image processing;
  • The propagation of uncertainty in remote sensing applications;
  • Case studies that demonstrate the importance of uncertainty analysis in remote sensing applications.

Dr. Penglin Zhang
Dr. Qi Zhang
Prof. Dr. Jon Atli Benediktsson
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • image processing and uncertainty analysis
  • uncertainty in remote sensing applications
  • uncertainty quantification
  • uncertainty suppression/control
  • error or uncertainty modeling
  • process or mechanism of uncertainty propagation
  • data quality
  • reliability
  • the impact of uncertainty on decision making
  • geospatial statistics

Related Special Issue

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 2721 KiB  
Article
Probabilistic Estimation of Tropical Cyclone Intensity Based on Multi-Source Satellite Remote Sensing Images
by Tao Song, Kunlin Yang, Xin Li, Shiqiu Peng and Fan Meng
Remote Sens. 2024, 16(4), 606; https://doi.org/10.3390/rs16040606 - 06 Feb 2024
Viewed by 607
Abstract
Estimating the intensity of tropical cyclones (TCs) is beneficial for preventing and reducing the impact of natural disasters. Most existing methods for estimating TC intensity utilize single-satellite or single-band remote sensing images, but they lack the ability to quantify the uncertainty of the [...] Read more.
Estimating the intensity of tropical cyclones (TCs) is beneficial for preventing and reducing the impact of natural disasters. Most existing methods for estimating TC intensity utilize single-satellite or single-band remote sensing images, but they lack the ability to quantify the uncertainty of the estimation results. However, TC, as a typical chaotic system, often requires confidence intervals for intensity estimates in real-world emergency decision-making scenarios. Additionally, the use of multi-source image inputs contributes to the uncertainty of the model. Consequently, this study introduces a neural network (MTCIE) that utilizes multi-source satellite images to provide probabilistic estimates of TC intensity. The model utilizes infrared and microwave images from multiple satellites as inputs. It uses a dual-branch self-attention encoder to extract TC image features and provides uncertainty estimates for TC intensity. Furthermore, a dataset for estimating the intensity of multi-source TC remote sensing images (MTCID) is constructed through the registration of latitude, longitude, and time, along with data augmentation. The proposed method achieves a MAE of 7.42 kt in deterministic estimation, comparable to mainstream networks like TCIENet. In uncertain estimation, it outperforms methods like MC Dropout in the PICP metric, providing reliable probability estimates. This supports TC disaster emergency decision making, enhancing risk mitigation in real-world applications. Full article
(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis II)
►▼ Show Figures

Figure 1

18 pages, 5029 KiB  
Article
Integrating Multi-Point Geostatistics, Machine Learning, and Image Correlation for Characterizing Positional Errors in Remote-Sensing Images of High Spatial Resolution
by Liang Xin, Wangle Zhang, Jianxu Wang, Sijian Wang and Jingxiong Zhang
Remote Sens. 2023, 15(19), 4734; https://doi.org/10.3390/rs15194734 - 27 Sep 2023
Viewed by 853
Abstract
Remote-sensing images of high spatial resolution (HSR) are valuable sources of fine-grained spatial information for various applications, such as urban surveys and governance. There is continuing research on positional errors in remote-sensing images and their impacts in geoprocessing and applications. This paper explores [...] Read more.
Remote-sensing images of high spatial resolution (HSR) are valuable sources of fine-grained spatial information for various applications, such as urban surveys and governance. There is continuing research on positional errors in remote-sensing images and their impacts in geoprocessing and applications. This paper explores the combined use of multi-point geostatistics (MPS), machine learning—in particular, generalized additive modeling (GAM)—and computer-image correlation for characterizing positional errors in images—in particular, HSR images. These methods are employed because of the merits of MPS in being flexible for non-parametric and joint simulation of positional errors in X and Y coordinates, the merits of GAM in being capable of handling non-stationarity in-positional errors through error de-trending, and the merits of computer-image correlation in being cost-effective in furnishing the training data (TD) required in MPS. Procedurally, image correlation is applied to identify homologous image points in reference-test image pairs to extract image displacements automatically in constructing TD. To cope with the complexity of urban scenes and the unavailability of truly orthorectified images, visual screening is performed to clean the raw displacement data to create quality-enhanced TD, while manual digitization is used to obtain reference sample data, including conditioning data (CD), for MPS and test data for performance evaluation. GAM is used to decompose CD and TD into trends and residuals. With CD and TD both de-trended, the direct sampling (DS) algorithm for MPS is applied to simulate residuals over a simulation grid (SG) at 80 m spatial resolution. With the realizations of residuals and, hence, positional errors generated in this way, the means, standard deviation, and cross correlation in bivariate positional errors at SG nodes are computed. The simulated error fields are also used to generate equal-probable realizations of vertices that define some road centerlines (RCLs), selected for this research through interpolation over the aforementioned simulated error fields, leading to error metrics for the RCLs and for the lengths of some RCL segments. The enhanced georectification of the RCLs is facilitated through error correction. A case study based in Shanghai municipality, China, was carried out, using HSR images as part of generalized point clouds that were developed. The experiment results confirmed that by using the proposed methods, spatially explicit positional-error metrics, including means, standard deviation, and cross correlation, can be quantified flexibly, with those in the selected RCLs and the lengths of some RCL segments derived easily through error propagation. The reference positions of these RCLs were obtained through error correction. The positional accuracy gains achieved by the proposed methods were found to be comparable with those achieved by conventional image georectification, in which the CD were used as image-georectification control data. The proposed methods are valuable not only for uncertainty-informed image geolocation and analysis, but also for integrated geoinformation processing. Full article
(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis II)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop